

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class 							_UpperCAmelCase			( _A      ):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE				:					Tuple          =					(CMStochasticIterativeScheduler,)
  SCREAMING_SNAKE_CASE				:					List[str]          =					10







  def 						UpperCamelCase							(			self						:			Optional[int]		,    **UpperCamelCase__						:			List[str]			):
        A 			=					{
            """num_train_timesteps""": 201,
            """sigma_min""": 0.002,
            """sigma_max""": 80.0,
        }

        config.update(**snake_case_			)
        return config







  def 						UpperCamelCase							(			self						:			Tuple			):
        A 			=					10

        A 			=					self.get_scheduler_config()
        A 			=					self.scheduler_classes[0](**snake_case_			)

        scheduler.set_timesteps(snake_case_			)

        A 			=					scheduler.timesteps[0]
        A 			=					scheduler.timesteps[1]

        A 			=					self.dummy_sample
        A 			=					0.1 * sample

        A 			=					scheduler.step(snake_case_		,    snake_case_		,    snake_case_			).prev_sample
        A 			=					scheduler.step(snake_case_		,    snake_case_		,    snake_case_			).prev_sample

        self.assertEqual(output_a.shape		,    sample.shape			)
        self.assertEqual(output_a.shape		,    output_a.shape			)







  def 						UpperCamelCase							(			self						:			Optional[Any]			):
        for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=snake_case_			)







  def 						UpperCamelCase							(			self						:			Optional[Any]			):
        for clip_denoised in [True, False]:
              self.check_over_configs(clip_denoised=snake_case_			)







  def 						UpperCamelCase							(			self						:			Any			):
        A 			=					self.scheduler_classes[0]
        A 			=					self.get_scheduler_config()
        A 			=					scheduler_class(**snake_case_			)

        A 			=					1
        scheduler.set_timesteps(snake_case_			)
        A 			=					scheduler.timesteps

        A 			=					torch.manual_seed(0			)

        A 			=					self.dummy_model()
        A 			=					self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(snake_case_			):
              # 1. scale model input
              A 			=					scheduler.scale_model_input(snake_case_		,    snake_case_			)

              # 2. predict noise residual
              A 			=					model(snake_case_		,    snake_case_			)

              # 3. predict previous sample x_t-1
              A 			=					scheduler.step(snake_case_		,    snake_case_		,    snake_case_		,    generator=snake_case_			).prev_sample

              A 			=					pred_prev_sample

        A 			=					torch.sum(torch.abs(snake_case_			)			)
        A 			=					torch.mean(torch.abs(snake_case_			)			)

        assert abs(result_sum.item() - 192.7_614			) < 1e-2
        assert abs(result_mean.item() - 0.2_510			) < 1e-3







  def 						UpperCamelCase							(			self						:			str			):
        A 			=					self.scheduler_classes[0]
        A 			=					self.get_scheduler_config()
        A 			=					scheduler_class(**snake_case_			)

        A 			=					[106, 0]
        scheduler.set_timesteps(timesteps=snake_case_			)
        A 			=					scheduler.timesteps

        A 			=					torch.manual_seed(0			)

        A 			=					self.dummy_model()
        A 			=					self.dummy_sample_deter * scheduler.init_noise_sigma

        for t in timesteps:
              # 1. scale model input
              A 			=					scheduler.scale_model_input(snake_case_		,    snake_case_			)

              # 2. predict noise residual
              A 			=					model(snake_case_		,    snake_case_			)

              # 3. predict previous sample x_t-1
              A 			=					scheduler.step(snake_case_		,    snake_case_		,    snake_case_		,    generator=snake_case_			).prev_sample

              A 			=					pred_prev_sample

        A 			=					torch.sum(torch.abs(snake_case_			)			)
        A 			=					torch.mean(torch.abs(snake_case_			)			)

        assert abs(result_sum.item() - 347.6_357			) < 1e-2
        assert abs(result_mean.item() - 0.4_527			) < 1e-3







  def 						UpperCamelCase							(			self						:			str			):
        A 			=					self.scheduler_classes[0]
        A 			=					self.get_scheduler_config()
        A 			=					scheduler_class(**snake_case_			)

        A 			=					[39, 30, 12, 15, 0]

        with self.assertRaises(snake_case_		,    msg='`timesteps` must be in descending order.'			):
              scheduler.set_timesteps(timesteps=snake_case_			)







  def 						UpperCamelCase							(			self						:			str			):
        A 			=					self.scheduler_classes[0]
        A 			=					self.get_scheduler_config()
        A 			=					scheduler_class(**snake_case_			)

        A 			=					[39, 30, 12, 1, 0]
        A 			=					len(snake_case_			)

        with self.assertRaises(snake_case_		,    msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
              scheduler.set_timesteps(num_inference_steps=snake_case_		,    timesteps=snake_case_			)







  def 						UpperCamelCase							(			self						:			int			):
        A 			=					self.scheduler_classes[0]
        A 			=					self.get_scheduler_config()
        A 			=					scheduler_class(**snake_case_			)

        A 			=					[scheduler.config.num_train_timesteps]

        with self.assertRaises(
            snake_case_		,    msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'		,    ):
              scheduler.set_timesteps(timesteps=snake_case_			)





from __future__ import annotations

import time

lowerCamelCase_    :			Union[str, Any]    =	list[tuple[int, int]]

lowerCamelCase_    :			str    =	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCamelCase_    :			Dict    =	[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Dict    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							Node | None   ):
							UpperCamelCase_:				str						=    pos_x
							UpperCamelCase_:				List[str]						=    pos_y
							UpperCamelCase_:				str						=    (pos_y, pos_x)
							UpperCamelCase_:				Any						=    goal_x
							UpperCamelCase_:				Optional[int]						=    goal_y
							UpperCamelCase_:				Union[str, Any]						=    parent




class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							str    ,						snake_case_			:							tuple[int, int]    ,						snake_case_			:							tuple[int, int]   ):
							UpperCamelCase_:				Optional[Any]						=    Node(start[1]    ,						start[0]    ,						goal[1]    ,						goal[0]    ,						snake_case_   )
							UpperCamelCase_:				List[str]						=    Node(goal[1]    ,						goal[0]    ,						goal[1]    ,						goal[0]    ,						snake_case_   )

							UpperCamelCase_:				Any						=    [self.start]
							UpperCamelCase_:				Dict						=    False

			def    lowerCAmelCase__						(							self			:							str   ):
							while self.node_queue:
											UpperCamelCase_:				int						=    self.node_queue.pop(0   )

											if current_node.pos == self.target.pos:
															UpperCamelCase_:				List[str]						=    True
															return self.retrace_path(snake_case_   )

											UpperCamelCase_:				List[str]						=    self.get_successors(snake_case_   )

											for node in successors:
															self.node_queue.append(snake_case_   )

							if not self.reached:
											return [self.start.pos]
							return None

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Node   ):
							UpperCamelCase_:				int						=    []
							for action in delta:
											UpperCamelCase_:				Union[str, Any]						=    parent.pos_x + action[1]
											UpperCamelCase_:				str						=    parent.pos_y + action[0]
											if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(snake_case_   ) - 1):
															continue

											if grid[pos_y][pos_x] != 0:
															continue

											successors.append(
											    Node(snake_case_    ,						snake_case_    ,						self.target.pos_y    ,						self.target.pos_x    ,						snake_case_   )   )
							return successors




			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							Node | None   ):
							UpperCamelCase_:				int						=    node
							UpperCamelCase_:				Tuple						=    []
							while current_node is not None:
											path.append((current_node.pos_y, current_node.pos_x)   )
											UpperCamelCase_:				List[Any]						=    current_node.parent
							path.reverse()
							return path




class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Optional[Any]    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Optional[Any]   ):
							UpperCamelCase_:				Tuple						=    BreadthFirstSearch(snake_case_    ,						snake_case_   )
							UpperCamelCase_:				Dict						=    BreadthFirstSearch(snake_case_    ,						snake_case_   )
							UpperCamelCase_:				int						=    False

			def    lowerCAmelCase__						(							self			:							int   ):
							while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
											UpperCamelCase_:				List[Any]						=    self.fwd_bfs.node_queue.pop(0   )
											UpperCamelCase_:				Optional[Any]						=    self.bwd_bfs.node_queue.pop(0   )

											if current_bwd_node.pos == current_fwd_node.pos:
															UpperCamelCase_:				List[Any]						=    True
															return self.retrace_bidirectional_path(
															    snake_case_    ,						snake_case_   )

											UpperCamelCase_:				Optional[Any]						=    current_bwd_node
											UpperCamelCase_:				List[Any]						=    current_fwd_node

											UpperCamelCase_:				List[str]						=    {
											    self.fwd_bfs: self.fwd_bfs.get_successors(snake_case_   ),
											    self.bwd_bfs: self.bwd_bfs.get_successors(snake_case_   ),
											}

											for bfs in [self.fwd_bfs, self.bwd_bfs]:
															for node in successors[bfs]:
																			bfs.node_queue.append(snake_case_   )

							if not self.reached:
											return [self.fwd_bfs.start.pos]
							return None




			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							Node    ,						snake_case_			:							Node   ):
							UpperCamelCase_:				List[str]						=    self.fwd_bfs.retrace_path(snake_case_   )
							UpperCamelCase_:				Tuple						=    self.bwd_bfs.retrace_path(snake_case_   )
							bwd_path.pop()
							bwd_path.reverse()
							UpperCamelCase_:				List[str]						=    fwd_path + bwd_path
							return path


if __name__ == "__main__":
							# all coordinates are given in format [y,x]
							import doctest

							doctest.testmod()
							lowerCamelCase_    :			Tuple    =	(0, 0)
							lowerCamelCase_    :			Union[str, Any]    =	(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							lowerCamelCase_    :			Optional[int]    =	time.time()
							lowerCamelCase_    :			Tuple    =	BreadthFirstSearch(init, goal)
							lowerCamelCase_    :			List[str]    =	bfs.search()
							lowerCamelCase_    :			Optional[int]    =	time.time() - start_bfs_time

							print("""Unidirectional BFS computation time : """, bfs_time)

							lowerCamelCase_    :			Optional[int]    =	time.time()
							lowerCamelCase_    :			Optional[int]    =	BidirectionalBreadthFirstSearch(init, goal)
							lowerCamelCase_    :			str    =	bd_bfs.search()
							lowerCamelCase_    :			Tuple    =	time.time() - start_bd_bfs_time

							print("""Bidirectional BFS computation time : """, bd_bfs_time)






